2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献_第10页

Audio Replay Spoof Attack Detection Using A GMM-RFPNN Model as Back-end Classifier 基于GMM-RFPNN模型的音频重放欺骗攻击检测

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00089

Kaikai Qi, Wei Huang, Dan Wang, Honghao Zhang

{"title":"Audio Replay Spoof Attack Detection Using A GMM-RFPNN Model as Back-end Classifier","authors":"Kaikai Qi, Wei Huang, Dan Wang, Honghao Zhang","doi":"10.1109/ICAICE54393.2021.00089","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00089","url":null,"abstract":"Research on automatic speaker verification (ASV) techniques has received academic attention in recent years and has begun to be applied to authentication, but research on the security performance of ASV is just beginning. In this paper, we will focus on speech replay spoofing attack detection in speaker authentication techniques. Voice is a biological behavioral feature with high inter-class variability and susceptibility to environmental and temporal influences. In this paper, classical constant Q cepstral coefficient features (CQCC) and Gaussian super-vectors are used as front-end feature extractors and fuzzy polynomial neural network (FPNN) models with regularization processing are used as back-end classifiers for true and false speech detection. Compared with other traditional machine learning models and deep learning models, this model shows stronger robustness and generalization ability on acoustic environment and time variation, and good detection results can be obtained using a small number of samples for training. Tested on the ASV spoof 2017 version 2.0 database, the detection performance is improved by about 39% compared to the original baseline system.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128390471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A binocular location method based on feature point matching and region pixel matching 一种基于特征点匹配和区域像素匹配的双目定位方法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00126

Zheng-guang Xu, Yang Ye, Li Yuan, Xuan Zhu

引用次数: 0

Multi-scale modeling of the lithium battery energy storage system 锂电池储能系统的多尺度建模

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00067

H. Bai, Xiaobing Teng, Zhen Liu, Haoran Wang, D. Zhou

引用次数: 1

Error Correction of Tibetan Verbs Based on Deep Learning 基于深度学习的藏文动词纠错

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00051

Hua Guo-cai-rang, Secha Jia, Ban Ma-bao, Cai Rang-jia

引用次数: 0

Design of Smart Service System Based on Kano Model and Calculation of Importance of Function 基于Kano模型的智能服务系统设计及功能重要性计算

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00069

Qin Wang, Chengyuan Liu, Luping Pan

{"title":"Design of Smart Service System Based on Kano Model and Calculation of Importance of Function","authors":"Qin Wang, Chengyuan Liu, Luping Pan","doi":"10.1109/ICAICE54393.2021.00069","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00069","url":null,"abstract":"Objective To study the functional configuration of a smart service system that meets the needs of users, and to propose an improved research method. Methods the Kano model was used to obtain the functional classification of the smart service system based on user needs. The functions in the must-be attribute were retained, pairwise comparison method was used to construct the judgment matrix for the functions in the one-dimensional attribute and attractive attribute, and the square root method was used to calculate the weight of the importance of function, so as to determine the function configuration scheme of the system. Considering the smart kitchen service system as an example, the fuzzy comprehensive evaluation method was used to evaluate the design scheme, and thus verified the feasibility of the method. Conclusion from the perspective of users, combining the advantage of the Kano model in the classification and calculation of the importance of function could easily distinguish the priority of functions in complex systems, and provide a basis for the function design of smart service systems.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Motor Imagery Analysis Based on Filter Bank Common Spatial Pattern 基于滤波器组公共空间模式的运动图像分析

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00130

Yixin Du, Runtian Xu, Jiting Zhang

{"title":"Motor Imagery Analysis Based on Filter Bank Common Spatial Pattern","authors":"Yixin Du, Runtian Xu, Jiting Zhang","doi":"10.1109/icaice54393.2021.00130","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00130","url":null,"abstract":"This paper analyzes the EEG signals of left and right hand motor imagery and uses Filter Bank Common Spatial Pattern to extract features from EEG signals. Compared with Discrete Wavelet Transform, Autoregressive mode, Power Spectral Density and Common Spatial Pattern, it is found that FBCSP can significantly improve the recognition rate. While extracting the features of sub-frequency bands, this paper also considers ERD and ERS in motor imagery and the distribution of signal energy in different frequency bands. It is found that during the time of the motor imagery, the energy changes are generally concentrated on the μ rhythm, and the motor sensory area of the cerebral cortex has the nature of contralateral reflection of left and right hand motor imagery. Researchers generally use Support Vector Machine classifiers for pattern recognition classification, but the classification effect of a traditional SVM classifier is not ideal. Therefore, the paper uses the integration algorithm AdaBoost and algorithm Gradient Boosting for classification. Compared with Adaboost, the error rate of Gradient Boosting algorithm drops more sharply with the increased number of iterations, and the accuracy of Gradient Boosting algorithm is higher.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130520939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on the scheduling strategy of intelligent manufacturing workshop based on machine learning 基于机器学习的智能制造车间调度策略研究

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00016

Haonan Guan

引用次数: 0

Design and Implementation of Pork Freshness Grading Based on Deep Learning 基于深度学习的猪肉新鲜度分级设计与实现

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00050

Cong Wang, Cheng Lv, Run Li, Panpan Wang, Xiaodong Wang, A. Zhao, S. Jin, Bing Han, Shan Lu

{"title":"Design and Implementation of Pork Freshness Grading Based on Deep Learning","authors":"Cong Wang, Cheng Lv, Run Li, Panpan Wang, Xiaodong Wang, A. Zhao, S. Jin, Bing Han, Shan Lu","doi":"10.1109/icaice54393.2021.00050","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00050","url":null,"abstract":"China is the world's largest pork production and consumption country, with the improvement of people's living standards and consumption upgrade, people's demand for fresh pork and other fresh products is stronger. With the outbreak of African Swine Fever and COVID-19 in China in the past two years, cold chain transportation of pork will replace live pigs as the main mode of pork supply chain. As one of the most important branches of machine learning, deep learning has developed rapidly in recent years and attracted extensive attention at home and abroad. In order to improve the real-time detection of pork freshness, this paper experimented with a variety of deep learning frameworks to achieve pork freshness classification. In this paper, pork freshness is divided into 5 levels according to TVB-N content, and the pictures taken are trained by different deep learning networks, including VGG, GoogLeNet and RestNet. After analyzing the training situation of each network, the advantages of different networks are absorbed and a new improved neural network is built to predict pork freshness. The final classification accuracy reached 97%, Indicating that this is a very efficient and accurate pork freshness classification method.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116508140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A self-augmented radial basis function neural network for sensitive systems 敏感系统的自增径向基函数神经网络

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00077

Yanxia Yang, Pu Wang, Xuejin Gao, Huihui Gao

引用次数: 0

Using machine learning forecasts movie revenue 利用机器学习预测电影收入

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00094

Haibo Li

引用次数: 1